TY - JOUR
T1 - Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller
AU - Farrag, Mohamed
AU - Putrus, Ghanim
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PY - 2012/1
Y1 - 2012/1
N2 - This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller.
AB - This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller.
KW - artificial intelligence
KW - flexible ac transmission systems
KW - fuzzy
KW - neural networks
KW - unified power-flow controller (UPFC)
UR - https://www.scopus.com/pages/publications/84655161261
U2 - 10.1109/TPWRD.2011.2171061
DO - 10.1109/TPWRD.2011.2171061
M3 - Article
SN - 0885-8977
VL - 27
SP - 53
EP - 61
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
IS - 1
ER -